Title :
A Thousand Words in a Scene
Author :
Quelhas, Pedro ; Monay, Florent ; Odobez, Jean-Marc ; Gatica-Perez, Daniel ; Tuytelaars, Tinne
Author_Institution :
IDIAP Res. Inst., Martigny
Abstract :
This paper presents a novel approach for visual scene modeling and classification, investigating the combined use of text modeling methods and local invariant features. Our work attempts to elucidate (1) whether a textlike bag-of-visterms (BOV) representation (histogram of quantized local visual features) is suitable for scene (rather than object) classification, (2) whether some analogies between discrete scene representations and text documents exist, and 3) whether unsupervised, latent space models can be used both as feature extractors for the classification task and to discover patterns of visual co-occurrence. Using several data sets, we validate our approach, presenting and discussing experiments on each of these issues. We first show, with extensive experiments on binary and multiclass scene classification tasks using a 9,500-image data set, that the BOV representation consistently outperforms classical scene classification approaches. In other data sets, we show that our approach competes with or outperforms other recent more complex methods. We also show that probabilistic latent semantic analysis (PLSA) generates a compact scene representation, is discriminative for accurate classification, and is more robust than the BOV representation when less labeled training data is available. Finally, through aspect-based image ranking experiments, we show the ability of PLSA to automatically extract visually meaningful scene patterns, making such representation useful for browsing image collections.
Keywords :
feature extraction; image classification; image representation; image segmentation; probability; text analysis; feature extractor; image data set; latent space model; local invariant feature; probabilistic latent semantic analysis; scene classification; text modeling method; textlike bag-of-visterm representation; visual scene model; Content based retrieval; Data mining; Feature extraction; Histograms; Image retrieval; Image segmentation; Layout; Object recognition; Robustness; Training data; Image representation; latent aspect modeling; object recognition; quantized local descriptors; scene classification; Algorithms; Artificial Intelligence; Databases, Factual; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Natural Language Processing; Pattern Recognition, Automated; Reproducibility of Results; Sensitivity and Specificity;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
DOI :
10.1109/TPAMI.2007.1155